AbstractThe complexity of modern robotic workcells, consisting of many cooperating elements (e.g., robots, sensing systems) makes programming applications enormously difficult. Even when the application can be easily ...
AbstractThe complexity of modern robotic workcells, consisting of many cooperating elements (e.g., robots, sensing systems) makes programming applications enormously difficult. Even when the application can be easily decomposed into workcelll operations, the tight coupling among these operations due to constraints on precedence and resource sharing makes it difficult to identify and take advantage of possible concurrency. Since robotic workcells are typically configured to repetitively perform an application, we seek to minimize the ‘cycle’ time required, by optimizing the order in which operations take place. Eventually, a sequential program must be written for each workcell element, and they must then execute concurrently. Commercial robot languages and systems provide neither the necessary tools for programming such a distributed collection of elements nor a mechanism for inter‐element communication. In this article the authors propose a two‐part solution to this workcell programming problem: SAGE/WRAP. A graphics interface is provided to the user in order to define the application. Then, from the user's input, SAGE builds a Petri Net description of the workcell application; this is used to obtain a time‐optimal sequence of operations for each workcell element. The time‐optimal sequence then becomes the basis of the runtime program that performs the application. This program is interpreted and executed by the hierarchical workcell runtime environment called WRAP. An example is provided to illustrate t
An approach to finding the solution equations for simple manipulators is described which enhances the well known method of Paul, Renaud, and Stevenson, by explicitly making use of known decouplings in the manipulator ...
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Driver assistance systems that monitor driver intent, warn drivers of lane departures, or assist in vehicle guidance are all being actively research and even put into commercial production. It is therefore important t...
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The Contextual Activity Notification Visualization Analysis System (Canvas) provides a user interaction interface for instantaneous feedback of contextual processing units that enable high-level semantic extraction an...
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A mobile platform mounted with Omni-Directional vision Sensor (ODVS) can be used to monitor large areas and detect interesting events such as independently moving persons and vehicles. To avoid false alarms due to ext...
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ISBN:
(纸本)158113780X
A mobile platform mounted with Omni-Directional vision Sensor (ODVS) can be used to monitor large areas and detect interesting events such as independently moving persons and vehicles. To avoid false alarms due to extraneous features, the image motion induced by the moving platform should be compensated. This paper describes a formulation of parametric ego-motion compensation for an ODVS. Omni images give 360 degrees view of surroundings but undergo considerable image distortion. To account for these distortions, the parametric planar motion model is integrated with the transformations into omni image space. Prior knowledge of approximate camera calibration and vehicle speed are integrated with the estimation process using Bayesian approach. Iterative, coarse to fine, gradient based estimation is used to correct the motion parameters for vibrations and other inaccuracies in prior knowledge. Experiments with camera mounted on a mobile platform demonstrate successful detection of moving persons and vehicles. Copyright 2003 ACM.
In this paper we present a framework for tracking non-rigid facial landmarks by combining various visual cues at multiple levels of detail. Using a probabilistic framework consisting of a hierarchy of particle filters...
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In this paper we introduce the NDS-Forest data structure, which can be used for the calculation and representation of Maximally Stable Extremal Regions in real-time video. In contrast to the standard MSER algorithm, t...
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ISBN:
(纸本)1904410146
In this paper we introduce the NDS-Forest data structure, which can be used for the calculation and representation of Maximally Stable Extremal Regions in real-time video. In contrast to the standard MSER algorithm, the NDS-Forest stores information about the extremal regions as they are formed, making it unnecessary to regrow the regions from seed pixels. Using the NDS-Forest structure, we describe a system that uses MSERs in an automobile for face registration, segmentation, and pose estimation of the driver.
This paper describes how a surface reconstruction algorithm, based on minimizing the variation of surface curvature, can be used to stabilize and correct the results of local shading analysis. What is novel about this...
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This paper describes how a surface reconstruction algorithm, based on minimizing the variation of surface curvature, can be used to stabilize and correct the results of local shading analysis. What is novel about this approach is that it is viewpoint independent and applicable to any process that can provide estimates of local surface orientation. The assumptions used in formulating the minimization are derived from standard differential geometry. When applied as a second stage of processing after local shading analysis, the algorithm can recover a close approximation of the true surface orientation under realistic assumptions about image noise. Results are presented that show the performance of the algorithm on synthetic and real data. In particular, they demonstrate how this form of reconstruction can compensate for some of the shape distortion incurred in local shading analysis.
We describe an algorithm for segmenting a 3D object into its constituent volumetric parts from a single 2D line drawing. This algorithm is part of the bottom-up line drawing description process of PARVO, a generic obj...
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We describe an algorithm for segmenting a 3D object into its constituent volumetric parts from a single 2D line drawing. This algorithm is part of the bottom-up line drawing description process of PARVO, a generic object recognition system. In PARVO, an object is recognized as being a member of a class defined by qualitative geometrical properties and topological structure. Similarity between different 3D views of any member of a given class is a result of building coarse part-based structural descriptions from limited information. A preliminary stage to part segmentation of an object consists of extracting viewpoint-invariant and nonaccidental features from the line drawing. The segmentation process itself is described as a hierarchical decomposition of the line drawing on the basis of pairs of high-concavity points in its silhouette. The pairing of these so-called segmentation points rests on (i) an object visibility model describing the generic structures present in any line drawing satisfying our assumptions, and (ii) the feature instances extracted from the specific line drawing analyzed. First, we discuss the properties of the line drawings in the domain of PARVO. From them, geometrical models of important line drawing features may be developed. The latter are used both as cues in the segmentation process and as precursors to the representation from which part structure is understood.
The computation of 3-D structure from motion using a monocular sequence of images in the paradigm of active vision is investigated in this paper. Robotic tasks such as navigation, manipulation, and object recognition ...
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